Instructions to use tkeskin/llama-3.2-1b-instruct-code-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tkeskin/llama-3.2-1b-instruct-code-translation with Transformers:

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

pipe = pipeline("text-generation", model="tkeskin/llama-3.2-1b-instruct-code-translation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tkeskin/llama-3.2-1b-instruct-code-translation")
model = AutoModelForCausalLM.from_pretrained("tkeskin/llama-3.2-1b-instruct-code-translation")
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 tkeskin/llama-3.2-1b-instruct-code-translation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tkeskin/llama-3.2-1b-instruct-code-translation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tkeskin/llama-3.2-1b-instruct-code-translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tkeskin/llama-3.2-1b-instruct-code-translation

SGLang

How to use tkeskin/llama-3.2-1b-instruct-code-translation 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 "tkeskin/llama-3.2-1b-instruct-code-translation" \
    --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": "tkeskin/llama-3.2-1b-instruct-code-translation",
		"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 "tkeskin/llama-3.2-1b-instruct-code-translation" \
        --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": "tkeskin/llama-3.2-1b-instruct-code-translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tkeskin/llama-3.2-1b-instruct-code-translation with Docker Model Runner:
```
docker model run hf.co/tkeskin/llama-3.2-1b-instruct-code-translation
```

llama-3.2-1b-instruct-code-translation

A fine-tuned version of meta-llama/Llama-3.2-1B-Instruct for translating code between C++, Java, and Python.

Training

Base model: meta-llama/Llama-3.2-1B-Instruct
Method: LoRA (Low-Rank Adaptation) via LLaMA-Factory
Dataset: tkeskin/leetcode-solutions (instruct config) — directed C++/Java/Python translation pairs derived from LeetCode solutions
Hardware: AMD MI210 (ROCm), flash_attn: sdpa
LoRA target: all linear layers (lora_target: all)
Precision: bf16

Intended use

Given source code in one of C++, Java, or Python, the model generates a translation into the target language, following the same logic and structure.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tkeskin/llama-3.2-1b-instruct-code-translation"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": "Translate the following C++ code to Python:\n\nint add(int a, int b) { return a + b; }"
    }
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for tkeskin/llama-3.2-1b-instruct-code-translation

Base model

meta-llama/Llama-3.2-1B-Instruct

Adapter

(646)

this model

tkeskin
/

llama-3.2-1b-instruct-code-translation

llama-3.2-1b-instruct-code-translation

Training

Intended use

Usage

Model tree for tkeskin/llama-3.2-1b-instruct-code-translation

Dataset used to train tkeskin/llama-3.2-1b-instruct-code-translation