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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
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?"
			}
		]
	}'

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
```

sandeeprdy1729 commited on 21 days ago

Commit

236e77a

verified ·

1 Parent(s): 4e862fc

Upload README.md with huggingface_hub

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README.md +145 -13

README.md CHANGED Viewed

@@ -1,21 +1,153 @@
 ---
-base_model: unsloth/qwen2.5-coder-0.5b-instruct-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-license: apache-2.0
 language:
 - en
 ---
-# Uploaded finetuned  model
-- **Developed by:** sandeeprdy1729
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-coder-0.5b-instruct-bnb-4bit
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
 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.