Instructions to use eunushosen/jonas-v2-0-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eunushosen/jonas-v2-0-2 with Transformers:

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

pipe = pipeline("text-generation", model="eunushosen/jonas-v2-0-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eunushosen/jonas-v2-0-2")
model = AutoModelForCausalLM.from_pretrained("eunushosen/jonas-v2-0-2")
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 eunushosen/jonas-v2-0-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eunushosen/jonas-v2-0-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eunushosen/jonas-v2-0-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eunushosen/jonas-v2-0-2

SGLang

How to use eunushosen/jonas-v2-0-2 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 "eunushosen/jonas-v2-0-2" \
    --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": "eunushosen/jonas-v2-0-2",
		"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 "eunushosen/jonas-v2-0-2" \
        --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": "eunushosen/jonas-v2-0-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eunushosen/jonas-v2-0-2 with Docker Model Runner:
```
docker model run hf.co/eunushosen/jonas-v2-0-2
```

jonas-7b-v2-0

Mistral-7B-Instruct-v0.3 fine-tuned to humanize text, with the LoRA adapter merged into the base weights. Serve with TGI or vLLM (OpenAI-compatible, supports streaming).

Prompt format (must match training)

Send the user message as:

Humanize the following text. Keep the meaning accurate; fix stiff phrasing and rhythm so it reads like something a thoughtful person wrote. Do not add any new information, facts, or opinions not present in the original text. The text type is: {text_type}

{text}

text_type ∈ {News, Academic, General, Formal, Casual, Legal, Simple}.

Example (OpenAI Messages API, streaming)

curl -N https://<ENDPOINT>/v1/chat/completions \
  -H "Authorization: Bearer $HF_TOKEN" -H "Content-Type: application/json" \
  -d '{"model":"tgi","stream":true,
       "messages":[{"role":"user","content":"Humanize the following text. ... The text type is: General\n\n---\nFurthermore, the data was utilized."}],
       "max_tokens":512,"temperature":0.7}'


The `pipeline_tag: text-generation` line is the one that un-greys TGI/vLLM in the engine dropdown — everything else is documentation.

Now let me wire it into the merge script. Let me check the shell wrapper first.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for eunushosen/jonas-v2-0-2

Base model

mistralai/Mistral-7B-v0.3

Finetuned

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

Finetuned

(500)

this model