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 22 days ago

Commit

5b45b80

verified ·

1 Parent(s): 9c3c62a

Unsloth Model Card

Browse files

Files changed (1) hide show

README.md +14 -32

README.md CHANGED Viewed

@@ -1,39 +1,21 @@
 ---
-language: [en]
 license: apache-2.0
-base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
-tags: [code, bug-fixing, qwen2.5-coder, mlx, timps, apple-silicon]
-pipeline_tag: text-generation
 ---
-# TIMPS-Coder-0.5B 🛠️
-Fine-tuned coding model for bug fixing by [Sandeep Reddy](https://github.com/Sandeeprdy1729) · TIMPS Brand · Made in India 🇮🇳
-## Benchmark
-| Model | Score |
-|-------|-------|
-| Base (Qwen/Qwen2.5-Coder-0.5B-Instruct) | 88.0% |
-| **TIMPS-Coder (this)** | **92.0%** |
-*10-task bug-fix benchmark: NullPointer, KeyError, AsyncBug, ScopeBug, RecursionError, etc.*
-## Quickstart — MLX (Mac M1/M2/M3)
-```bash
-pip install mlx-lm
-mlx_lm generate --model sandeeprdy1729/TIMPS-Coder-0.5B --max-tokens 500 --temp 0.1 \
-  --prompt '<|im_start|>system
-You are TIMPS-Coder. Explain the bug cause then show fixed code.<|im_end|>
-<|im_start|>user
-Fix null_pointer: My Spring @Autowired field is null<|im_end|>
-<|im_start|>assistant'
-```
-## Training
-- Base: `Qwen/Qwen2.5-Coder-0.5B-Instruct` | Method: LoRA rank=16 | HW: Mac M2 Air 8GB
-- Dataset: 30,000+ clean coding instruction pairs (Python, Java, JS, C++, Go, Rust)
-- Framework: MLX-LM | LR: 5e-6 | Iters: 3000
-## Links
-- GitHub: [Sandeeprdy1729](https://github.com/Sandeeprdy1729)
-- YouTube: [@sandeepreddythummala](https://youtube.com/@sandeepreddythummala)
-Apache 2.0 License

 ---
+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)