Instructions to use sandeeprdy1729/TIMPS-Coder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sandeeprdy1729/TIMPS-Coder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="sandeeprdy1729/TIMPS-Coder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
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]:]))

MLX

How to use sandeeprdy1729/TIMPS-Coder-0.5B with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("sandeeprdy1729/TIMPS-Coder-0.5B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use sandeeprdy1729/TIMPS-Coder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sandeeprdy1729/TIMPS-Coder-0.5B"
# 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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sandeeprdy1729/TIMPS-Coder-0.5B

SGLang

How to use sandeeprdy1729/TIMPS-Coder-0.5B 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-0.5B" \
    --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-0.5B",
		"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-0.5B" \
        --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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Pi new

How to use sandeeprdy1729/TIMPS-Coder-0.5B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sandeeprdy1729/TIMPS-Coder-0.5B"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sandeeprdy1729/TIMPS-Coder-0.5B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sandeeprdy1729/TIMPS-Coder-0.5B with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "sandeeprdy1729/TIMPS-Coder-0.5B"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sandeeprdy1729/TIMPS-Coder-0.5B

Run Hermes

hermes

MLX LM

How to use sandeeprdy1729/TIMPS-Coder-0.5B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "sandeeprdy1729/TIMPS-Coder-0.5B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "sandeeprdy1729/TIMPS-Coder-0.5B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "sandeeprdy1729/TIMPS-Coder-0.5B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use sandeeprdy1729/TIMPS-Coder-0.5B with Docker Model Runner:
```
docker model run hf.co/sandeeprdy1729/TIMPS-Coder-0.5B
```

TIMPS-Coder-0.5B / README.md

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	---
	language:
	- en
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
	tags:
	- code
	- bug-fixing
	- code-review
	- qwen2
	- lora
	- mlx
	- ollama
	- chatml
	pipeline_tag: text-generation
	library_name: transformers
	---

	# TIMPS-Coder v3 — Elite Bug-Fixing Assistant (0.5B)

	> A 0.5B parameter coding model fine-tuned to think before it codes — specialising in bug
	> analysis, code review, algorithm problem-solving, and agentic planning.
	> Built by [Sandeep Reddy](https://github.com/Sandeeprdy1729) · TIMPS · Made in India 🇮🇳

	[![HuggingFace](https://img.shields.io/badge/HuggingFace-TIMPS--Coder--0.5B-yellow)](https://huggingface.co/sandeeprdy1729/TIMPS-Coder-0.5B)
	[![Ollama](https://img.shields.io/badge/Ollama-sandeeprdy1729%2Ftimps--coder-blue)](https://ollama.com/sandeeprdy1729/timps-coder)
	[![License](https://img.shields.io/badge/license-Apache%202.0-green)](LICENSE)
	[![Benchmark](https://img.shields.io/badge/Internal%20Benchmark-88%25%20(44%2F50)-brightgreen)](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/benchmark_results.json)

	## Model Summary

	\| Field \| Value \|
	\|---\|---\|
	\| Base model \| `Qwen/Qwen2.5-Coder-0.5B-Instruct` (Alibaba Cloud) \|
	\| Architecture \| Qwen2 Transformer — 494M parameters \|
	\| Fine-tuning method \| LoRA (rank=16, 16 layers) via MLX-LM \|
	\| Context window \| 4096 tokens \|
	\| Quantization \| Q4_K_M GGUF (Ollama) / BF16 safetensors (HuggingFace) \|
	\| Chat template \| ChatML (`<\|im_start\|>` / `<\|im_end\|>`) \|
	\| License \| Apache 2.0 \|
	\| Training hardware \| Apple M-series (Mac M1/M2/M3, 8 GB RAM) \|

	## Benchmark Results — 25 Tests, 5 Dimensions

	Evaluated on [3_benchmark_ollama.py](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/3_benchmark_ollama.py).
	Scoring: 2 pts = complete correct answer with code · 1 pt = partial · 0 = wrong/refused.

	\| Dimension \| Score \| % \|
	\|---\|---\|---\|
	\| 🐛 Bug Fix \| 9 / 10 \| 90% \|
	\| 🔧 SWE / Repo-level \| 9 / 10 \| 90% \|
	\| ⚡ Algorithms \| 9 / 10 \| 90% \|
	\| 🔍 Code Review \| 8 / 10 \| 80% \|
	\| 🤖 Agentic Reasoning \| 9 / 10 \| 90% \|
	\| TOTAL \| 44 / 50 \| 88% \|

	## Quick Start

	### Ollama (recommended)

	```bash
	ollama pull sandeeprdy1729/timps-coder
	ollama run sandeeprdy1729/timps-coder
	```

	### Python (Transformers)

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
	tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")

	messages = [
	{"role": "system", "content": "You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases."},
	{"role": "user", "content": "Fix: `data['user']['email']` throws KeyError when email is absent."},
	]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt")
	out = model.generate(**inputs, max_new_tokens=700, temperature=0.1, do_sample=True)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```

	### MLX (Mac Apple Silicon)

	```bash
	pip install mlx-lm
	mlx_lm.generate \
	--model sandeeprdy1729/TIMPS-Coder-0.5B \
	--max-tokens 700 --temp 0.1 \
	--prompt '<\|im_start\|>system
	You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases.<\|im_end\|>
	<\|im_start\|>user
	Fix the race condition: two threads increment self.count += 1 simultaneously.<\|im_end\|>
	<\|im_start\|>assistant
	'
	```

	## Training Details

	### Fine-tuning Configuration

	\| Parameter \| Value \|
	\|---\|---\|
	\| Base model \| `Qwen/Qwen2.5-Coder-0.5B-Instruct` \|
	\| Fine-tuning method \| LoRA (Supervised Fine-Tuning) \|
	\| LoRA rank \| 16 \|
	\| Learning rate \| 5e-6 \|
	\| Iterations \| 3,000 \|
	\| Batch size \| 1 (grad accum ×4) \|
	\| Max sequence length \| 2048 tokens \|
	\| Framework \| MLX-LM on Apple Silicon \|
	\| Peak RAM \| ~5.5 GB \|

	### Training Data

	\| Dataset \| Type \| Approx. Samples \|
	\|---\|---\|---\|
	\| `newfacade/LeetCodeDataset` \| Algorithm problems with solutions \| ~2,500 \|
	\| `SWE-bench/SWE-bench_Verified` \| Real GitHub issue → patch \| ~400 \|
	\| `TIGER-Lab/SWE-Next-SFT-Trajectories` \| Agentic edit traces \| ~2,000 \|
	\| `WaltonFuture/agentic-sft-new` \| Tool use + bash planning \| ~3,000 \|
	\| Custom TIMPS bug-fix corpus \| Hand-curated bug/fix pairs \| ~500 \|
	\| Total \| \| ~8,400 samples \|

	All samples formatted in ChatML with `THINK → FIX → VERIFY` answer structure.

	## Capabilities

	\| Does well \| Limitations \|
	\|---\|---\|
	\| Bug root-cause analysis with explanation \| Complex multi-file refactors \|
	\| SQL injection, race condition, memory leak detection \| May miss subtle business-logic bugs \|
	\| O-notation analysis and algorithm optimisation \| Not a replacement for static analysis tools \|
	\| LeetCode medium-level algorithm problems \| Hard competitive programming problems \|
	\| GitHub Actions / CI YAML generation \| Not trained on Terraform, CDK \|

	## Usage Tips

	- Temperature: Keep at `0.1` — higher values increase hallucination on a 0.5B model
	- Context: Include the full function/class when asking for a bug fix
	- Verification: Always test generated code. Even at 88% accuracy, edge cases exist
	- System prompt: Required for best results — see the Quick Start examples above

	## Training Code

	Full training pipeline available at:
	[https://github.com/Sandeeprdy1729/TIMPS-Coder](https://github.com/Sandeeprdy1729/TIMPS-Coder)

	## License

	Apache 2.0 — free to use, modify, and distribute commercially.
	Base model (Qwen2.5-Coder-0.5B-Instruct) is also Apache 2.0.