Instructions to use silas114514/PMTX1-2B-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use silas114514/PMTX1-2B-merged with Transformers:

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

pipe = pipeline("text-generation", model="silas114514/PMTX1-2B-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("silas114514/PMTX1-2B-merged")
model = AutoModelForCausalLM.from_pretrained("silas114514/PMTX1-2B-merged")
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

vLLM

How to use silas114514/PMTX1-2B-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silas114514/PMTX1-2B-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silas114514/PMTX1-2B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/silas114514/PMTX1-2B-merged

SGLang

How to use silas114514/PMTX1-2B-merged 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 "silas114514/PMTX1-2B-merged" \
    --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": "silas114514/PMTX1-2B-merged",
		"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 "silas114514/PMTX1-2B-merged" \
        --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": "silas114514/PMTX1-2B-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use silas114514/PMTX1-2B-merged with Docker Model Runner:
```
docker model run hf.co/silas114514/PMTX1-2B-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PMTX1-2B Merged

PMTX1-2B Merged is a full merged-weight model derived from Qwen/Qwen3.5-2B + PMTX1 LoRA adapter.

Repository Type

This is a merged full-weight repo (not a LoRA adapter repo).

Contains at least:

model.safetensors
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja

One-Command Pull (Transformers)

pip install -U torch transformers
python -c "from huggingface_hub import snapshot_download; snapshot_download('silas114514/PMTX1-2B-merged')"

Quick Inference (Transformers)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "silas114514/PMTX1-2B-merged"

tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token or tok.unk_token

model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "你是 Prompt Evolution 的提示词纠偏教练。请只做提示词优化，不要直接代做任务。\n原始提示词：写周报，你看着办就行，快一点。"
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))

One-Command Pull (vLLM)

vllm serve silas114514/PMTX1-2B-merged --trust-remote-code

Base Model and Training Profile

Base model: Qwen/Qwen3.5-2B
Prompt style: train_aligned
LoRA layers: top 8
LoRA targets: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Training summary files included: run_summary.json, run_config.json, metrics_summary.json

Known Limitations

This model is optimized for prompt rewriting guidance, not unrestricted factual generation.
Some prompts may still need strict output post-processing for production format guarantees.
For Ollama: official qwen3.5 base can be pulled directly, but custom Qwen3.5 fine-tune portability to Ollama may require additional conversion/tooling support and validation.

License and Compliance

Follow upstream Qwen/Qwen3.5-2B license and usage terms.
This merged model is a derivative artifact from that base model.

Downloads last month: 5

Safetensors

Model size

2B params

Tensor type

F16

Model tree for silas114514/PMTX1-2B-merged

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

(180)

this model

Quantizations

1 model