Instructions to use tkeskin/mistral-7b-v0.3-code-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tkeskin/mistral-7b-v0.3-code-translation with Transformers:

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

pipe = pipeline("text-generation", model="tkeskin/mistral-7b-v0.3-code-translation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tkeskin/mistral-7b-v0.3-code-translation")
model = AutoModelForCausalLM.from_pretrained("tkeskin/mistral-7b-v0.3-code-translation", 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 tkeskin/mistral-7b-v0.3-code-translation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tkeskin/mistral-7b-v0.3-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/mistral-7b-v0.3-code-translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tkeskin/mistral-7b-v0.3-code-translation

SGLang

How to use tkeskin/mistral-7b-v0.3-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/mistral-7b-v0.3-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/mistral-7b-v0.3-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/mistral-7b-v0.3-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/mistral-7b-v0.3-code-translation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tkeskin/mistral-7b-v0.3-code-translation with Docker Model Runner:
```
docker model run hf.co/tkeskin/mistral-7b-v0.3-code-translation
```

mistral-7b-v0.3-code-translation

A fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 for translating code between C++, Java, and Python.

Training

Base model: mistralai/Mistral-7B-Instruct-v0.3
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) / NVIDIA CUDA, flash_attn: sdpa
LoRA target: all linear layers (lora_target: all)
Precision: bf16

Evaluation

Evaluated with an execution-based translation benchmark: each held-out evaluation-config payload from tkeskin/leetcode-solutions is a directed source→target translation whose output is compiled and run against the problem's input/output pairs. The eval split is held out from training (no leakage). Metric is pass@1 (all test cases pass), n-weighted over 3,336 payloads.

	Base (Mistral-7B-Instruct-v0.3)	This model	Δ
pass@1	11.8%	59.6%	+47.8
compile rate	45.0%	84.7%	+39.6

This is the largest gain in the series — the base model barely produces compilable C++/Java, and fine-tuning lifts it to near the top. pass@1 by language pair × difficulty (%):

source	target	difficulty	base	this model
cpp	java	Easy	9.7	81.4
cpp	java	Hard	3.4	47.5
cpp	java	Medium	8.1	72.9
cpp	python	Easy	29.7	61.6
cpp	python	Hard	16.0	45.8
cpp	python	Medium	27.3	64.3
java	cpp	Easy	2.7	79.6
java	cpp	Hard	2.5	51.3
java	cpp	Medium	4.1	70.0
java	python	Easy	4.1	64.5
java	python	Hard	6.1	45.8
java	python	Medium	8.4	62.3
python	cpp	Easy	19.7	64.6
python	cpp	Hard	10.1	26.9
python	cpp	Medium	17.8	51.9
python	java	Easy	17.2	64.8
python	java	Hard	2.5	28.8
python	java	Medium	8.5	53.1

Full methodology is in the llm-fine-tune repo (Stage 5).

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/mistral-7b-v0.3-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))

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for tkeskin/mistral-7b-v0.3-code-translation

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(838)

this model

tkeskin
/

mistral-7b-v0.3-code-translation